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The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle–mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license. [ABSTRACT FROM AUTHOR]

Datafication is driving organizations to invest in data commons, not only to share the costs of data generation, analysis, and curation but, more importantly, to realize synergies in precompetitive research collaborations where private and public motives interact (i.e., semicommons). The fanfare surrounding datafication often hails the sophisticated algorithms used to develop large quantities of data toward greater insight, naively assuming that more data equals better data. Yet for datafication in general and precompetitive research specifically, less attention is awarded to what actually constitutes data and evidence in the first place—that is, to its genesis, construction, and interpretation by heterogeneous scientific and commercial entities. We present the case of Open Targets, a precompetitive collaboration in the life sciences, where publicly funded research, nonprofit foundations, and for-profit pharma collaborate to generate and share data in genomics, proteomics, and bioinformatics. We theorize about the process of data commoning, a political activity in the semicommons where data are created, evidential value is assembled, and scientific meaning converges as data travels, or journeys, across creators, validators, and users. Our findings highlight the effects of relational dynamics and the political nature of data journeys: why these dynamics form, how they are manifested in a precompetitive semicommons, and what implications this can have for the mobility of data as a shared, public good. [ABSTRACT FROM AUTHOR]

In pharmaceutical discovery, the "magic methyl" effect describes a substantial improvement in the pharmacological properties of a drug candidate with the incorporation of methyl groups. Therefore, to expedite the synthesis of methylated drug analogs, late-stage, undirected methylations of C(sp3)-H bonds in complex molecules would be valuable. However, current methods for site-selective methylations are limited to activated C(sp3)-H bonds. Here we describe a site-selective, undirected methylation of unactivated C(sp3)-H bonds, enabled by photochemically activated peroxides and a nickel(II) complex whose turnover is enhanced by an ancillary ligand. The methodology displays compatibility with a wide range of functional groups and a high selectivity for tertiary C-H bonds, making it suitable for the late-stage methylation of complex organic compounds that contain multiple alkyl C-H bonds, such as terpene natural products, peptides, and active pharmaceutical ingredients. Overall, this method provides a synthetic tool to explore the "magic methyl" effect in drug discovery.

Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (kVR), capture structure-activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF-aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF-aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

BackgroundRestraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan.MethodsUsing Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture.ResultsWe developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo.ConclusionsOur method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

We previously reported trisubstituted pyrimidine lead compounds, namely, ARN22089 and ARN25062, which block the interaction between CDC42 with its specific downstream effector, a PAK protein. This interaction is crucial for the progression of multiple tumor types. Such inhibitors showed anticancer efficacy in vivo. Here, we describe a second class of CDC42 inhibitors with favorable drug-like properties. Out of the 25 compounds here reported, compound 15 (ARN25499) stands out as the best lead compound with an improved pharmacokinetic profile, increased bioavailability, and efficacy in an in vivo PDX tumor mouse model. Our results indicate that these CDC42 inhibitors represent a promising chemical class toward the discovery of anticancer drugs, with ARN25499 as an additional lead candidate for preclinical development.

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with budget BBs.

The global surge in multi-drug resistant bacteria, including extended-spectrum β-lactamase (ESBL)-producing Escherichia coli has led to a growing need for new antibacterial compounds. Despite being promising, the potential of fish-derived antimicrobial peptides (AMPs) in combating ESBL-producing E. coli is largely unexplored. In this study, native African catfish antimicrobial peptides (NACAPs) were extracted from the skin mucus of farmed African catfish, Clarias gariepinus, using a combination of 10% acetic acid solvent hydrolysis, 5 kDa ultrafiltration, and C18 hydrophobic interaction chromatography. Peptides were then sequenced using Orbitrap Fusion Lumos Tribrid Mass Spectrometry. The identified peptides were screened for potential antibacterial activity using Random Forest and AdaBoost machine learning algorithms. The most promising peptide was chemically synthesized and evaluated in vitro for safety on rabbit red blood cells and activity against ESBL-producing E. coli (ATCC 35218) utilizing spot-on-lawn and broth dilution methods. Eight peptides ranging from 13 to 22 amino acids with molecular weights between 968.42 and 2434.11 Da were identified. Peptide NACAP-II was non-hemolytic to rabbit erythrocytes (p > 0.05) with a zone of inhibition (ZOI) of 22.7 ± 0.9 mm and a minimum inhibitory concentration (MIC) of 91.3 ± 1.2 μg/mL. The peptide is thus a candidate antibacterial compound with enormous potential applications in the pharmaceutical industry. However, further studies are still required to establish an upscale production strategy and optimize its activity and safety in vivo.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Auranofin, an FDA-approved drug for rheumatoid arthritis, has emerged as a promising antiparasitic medication in recent years. The gold(I) ion in auranofin is postulated to be responsible for its antiparasitic activity. Notably, aurothiomalate and aurothioglucose also contain gold(I), and, like auranofin, they were previously used to treat rheumatoid arthritis. Whether they have antiparasitic activity remains to be elucidated. Herein, we demonstrated that auranofin and similar derivatives, but not aurothiomalate and aurothioglucose, inhibited the growth of Toxoplasma gondii in vitro. We found that auranofin affected the T. gondii biological cycle (lytic cycle) by inhibiting T. gondiis invasion and triggering its egress from the host cell. However, auranofin could not prevent parasite replication once T. gondii resided within the host. Auranofin treatment induced apoptosis in T. gondii parasites, as demonstrated by its reduced size and elevated phosphatidylserine externalization (PS). Notably, the gold from auranofin enters the cytoplasm of T. gondii, as demonstrated by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDS) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).IMPORTANCEToxoplasmosis, caused by Toxoplasma gondii, is a devastating disease affecting the brain and the eyes, frequently affecting immunocompromised individuals. Approximately 60 million people in the United States are already infected with T. gondii, representing a population at-risk of developing toxoplasmosis. Recent advances in treating cancer, autoimmune diseases, and organ transplants have contributed to this at-risk populations exponential growth. Paradoxically, treatments for toxoplasmosis have remained the same for more than 60 years, relying on medications well-known for their bone marrow toxicity and allergic reactions. Discovering new therapies is a priority, and repurposing FDA-approved drugs is an alternative approach to speed up drug discovery. Herein, we report the effect of auranofin, an FDA-approved drug, on the biological cycle of T. gondii and how both the phosphine ligand and the gold molecule determine the anti-parasitic activity of auranofin and other gold compounds. Our studies would contribute to the pipeline of candidate anti-T. gondii agents.

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.

In recent years, advances in biomedicine have revealed an important role for post-transcriptional mechanisms of gene expression regulation in pathologic conditions. In cancer in general and leukaemia specifically, RNA binding proteins have emerged as important regulator of RNA homoeostasis that are often dysregulated in the disease state. Having established the importance of these pathogenetic mechanisms, there have been a number of efforts to target RNA binding proteins using oligonucleotide-based strategies, as well as with small organic molecules. The field is at an exciting inflection point with the convergence of biomedical knowledge, small molecule screening strategies and improved chemical methods for synthesis and construction of sophisticated small molecules. Here, we review the mechanisms of post-transcriptional gene regulation, specifically in leukaemia, current small-molecule based efforts to target RNA binding proteins, and future prospects.

α-Glucosidase (α-Glu) is implicated in the progression and pathogenesis of type II diabetes (T2D). In this study, we developed a rapid colorimetric technique using platinum nanoparticles stabilized by chitosan (Ch-PtNPs) to detect α-Glu activity and its inhibitor. The Ch-PtNPs facilitate the conversion of 3,3′,5,5′-tetramethylbenzidine (TMB) into oxidized TMB (oxTMB) in the presence of dissolved O2. The catalytic hydrolysis of 2-O-α-d-glucopyranosyl-l-ascorbic acid (AA-2G) by α-Glu produces ascorbic acid (AA), which reduces oxTMB to TMB, leading to the fading of the blue color. However, the presence of α-Glu inhibitors (AGIs) hinders the generation of AA, allowing Ch-PtNPs to re-oxidize colorless TMB back to blue oxTMB. This unique phenomenon enables the colorimetric detection of α-Glu activity and AGIs. The linear range for α-Glu was found to be 0.1–1.0 U mL−1 and the detection limit was 0.026 U mL−1. Additionally, the half-maximal inhibition value (IC50) for acarbose, an α-Glu inhibitor, was calculated to be 0.4769 mM. Excitingly, this sensing platform successfully detected α-Glu activity in human serum samples and effectively screened AGIs. These promising findings highlight the potential application of the proposed strategy in clinical diabetes diagnosis and drug discovery. [ABSTRACT FROM AUTHOR]

Coupling reactions play a crucial role in drug development enabling the rapid expansion of structure–activity relationships (SARs) during drug discovery programs to identify a clinical candidate and simplify subsequent drug development processes. In particular, their relevance in clinical medicine and drug discovery has increased significantly in the last two decades. To facilitate these metal-catalyzed coupling reactions, suitably designed ligands are necessary and from the industrial point of view, sustainable and cost-effective ligands are of current need. Benzotriazole, a non-toxic, thermally stable, and inexpensive bidentate ligand, exhibits strong electron donating and electron accepting properties, along with excellent solubility in various organic solvents. It has been extensively explored as a synthetic auxiliary in the past; in recent years, benzotriazole and its derivatives have been used as ligands in metal-catalyzed coupling reactions. The facile construction of carbon–carbon and carbon–heteroatom bonds in the presence of versatile benzotriazole ligands makes it an indispensable ligand for catalytic transformations. The present feature article mainly emphasizes the advances in the utilization of benzotriazole as a ligand in a diverse range of C–C, C–N, C–O, and C–S coupling reactions. [ABSTRACT FROM AUTHOR]

Target-first drug discovery relies heavily on protein structure information, which severely limits its application. In recent years, fragment-based drug Design (FBDD) has been identified as an alternative solution, where screening of smaller molecules for lower affinity allowed the use of focused libraries with a higher hit rate. It is shown that coupling an sp2-rich heteroaromatic group with a monofunctional sp3-rich core gives fragments (186 examples) with advantageous physical-chemical properties, covering a chemical space often neglected in traditional libraries. [ABSTRACT FROM AUTHOR]

Substance use disorder (SUD) is a chronic condition, with maintained abuse of a substance leading to physiological and psychological alterations and often changes in cognitive and social behaviours. Current therapies include psychotherapy coupled with medication; however, high relapse rates reveal the shortcomings of these therapies. The signalling, expression profile, and neurological function of the serotonin 2C receptor (5‐HT2C receptor) make it a candidate of interest for the treatment of SUD. Recently, psychedelics, which broadly act at 5‐HT2 receptors, have indicated potential for the treatment of SUD, implicating the 5‐HT2C receptor. The modern psychedelic movement has rekindled interest in the 5‐HT2C receptor, resulting in many new studies, especially structural analyses. This review explores the structural, molecular and cellular mechanisms governing 5‐HT2C receptor function in the context of SUD. This provides the basis of the preclinical and clinical evidence for their role in SUD and highlights the potential for future exploration. [ABSTRACT FROM AUTHOR]

In this Perspective, the use of DNA nanotechnology is explored as a powerful tool for studying a family of enzymes known as topoisomerases. These enzymes regulate DNA topology within a living cell and play a major role in the pharmaceutical field, serving as anti‐cancer and anti‐bacterial targets. This Perspective will provide a short historical overview of the methods employed in studying these enzymes and emphasizing recent advancements in assays using DNA nanotechnology. These innovations have substantially improved accuracy and expanded the understanding of enzyme activity. This perspective will showcase the versatile utility of DNA nanotechnology in advancing scientific knowledge and its application in exploring new drug candidates, particularly in the study of topoisomerase enzymes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Abnormal activation of Group I mGluRs induces central sensitization. • Blocking Group Ⅰ mGluRs can alleviate neuropathic pain. • The existing Group I mGluRs orthosteric site antagonists have limitations. • Allosteric Modulators exhibit natural advantages due to allosteric modulation. • Structure based drug discovery may promote drug discovery targeting Group I mGluRs. Neuropathic pain (NP) is a widespread public health problem that existing therapeutic treatments cannot manage adequately; therefore, novel treatment strategies are urgently required. G-protein-coupled receptors are important for intracellular signal transduction, and widely participate in physiological and pathological processes, including pain perception. Group I metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, are predominantly implicated in central sensitization, which can lead to hyperalgesia and allodynia. Many orthosteric site antagonists targeting Group I mGluRs have been found to alleviate NP, but their poor efficacy, low selectivity, and numerous side effects limit their development in NP treatment. Here we reviewed the advantages of Group I mGluRs negative allosteric modulators (NAMs) over orthosteric site antagonists based on allosteric modulation mechanism, and the challenges and opportunities of Group I mGluRs NAMs in NP treatment. This article aims to elucidate the advantages and future development potential of Group I mGluRs NAMs in the treatment of NP. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the concentration of a drug and its therapeutic efficacy or side effects is crucial in drug development, especially to understand therapeutic efficacy in central nervous system drug, quantifying drug-induced site-specific changes in the levels of endogenous metabolites, such as neurotransmitters. In recent times, evaluation of quantitative distribution of drugs and endogenous metabolites using matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) has attracted much attention in drug discovery research. However, MALDI-MSI quantification (quantitative mass spectrometry imaging, QMSI) is an emerging technique, and needs to be further developed for practicable and convenient use in drug discovery research. In this study, we developed a reliable QMSI method for quantification of clozapine (antipsychotic drug) and dopamine and its metabolites in the rat brain using MALDI-MSI. An improved mimetic tissue model using powdered frozen tissue for QMSI was established as an alternative method, enabling the accurate quantification of clozapine levels in the rat brain. Furthermore, we used the improved method to evaluate drug-induced fluctuations in the concentrations of dopamine and its metabolites. This method can quantitatively evaluate drug localization in the brain and drug-induced changes in the concentration of endogenous metabolites, demonstrating the usefulness of QMSI. [ABSTRACT FROM AUTHOR]

Solid cancers frequently relapse with distant metastasis, despite local and systemic treatment. Cellular dormancy has been identified as an important mechanism underlying drug resistance enabling late relapse. Therefore, relapse from invisible, minimal residual cancer of seemingly disease-free patients call for in vitro models of dormant cells suited for drug discovery. Here, we explore dormancy-inducing 3D engineered matrices, which generate mechanical confinement and induce growth arrest and survival against chemotherapy in cancer cells. We characterized the dormant phenotype of solitary cells by P-ERKlow: P-p38high dormancy signaling ratio, along with Ki67-expression. As underlying mechanism, we identified stiffness-dependent nuclear localization of the four-and-a-half LIM domain 2 (FHL2) protein, leading to p53-independent high p21Cip1/Waf1 nuclear expression, validated in murine and human tissue. Suggestive of a resistance-causing role, cells in the dormancy-inducing matrix became sensitive against chemotherapy upon FHL2 down-regulation. Thus, our biomaterial-based approach will enable systematic screens for previously unidentified compounds suited to eradicate potentially relapsing dormant cancer cells. [ABSTRACT FROM AUTHOR]

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB. Scientific contribution: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions. [ABSTRACT FROM AUTHOR]

Background Study Design and Methods Results Conclusion The characteristic feature of immune cytopenias involves the process of extravascular phagocytosis, wherein macrophages in the spleen and/or liver engage in the destruction of blood cells that have been opsonized by auto‐ or alloantibodies. Therefore, new treatments that prevent phagocytosis will be advantageous, especially for short‐term usage along with alternative options.KB‐208, a small molecule drug, previously shown to be efficacious for the in vitro inhibition of phagocytosis was synthesized. A passive antibody mouse model of immune thrombocytopenia (ITP) was used. Three different mouse strains (BALB/c, C57BL/6, CD1) were used to determine the efficacy of KB‐208 compared with IVIG to ameliorate the ITP. Toxicity was investigated after 60‐day chronic administration of KB‐208 by a biochemistry panel, gross necroscopy and histopathology.KB‐208 showed similar efficacy to ameliorate the thrombocytopenia compared with IVIG in all three mouse strains. This small molecule drug was effective at 1 mg/kg in ameliorating ITP, in comparison with IVIG at 1000–2500 mg/kg. KB‐208 did not affect other blood parameters or elevate serum biochemistry markers of toxicity nor were any abnormal histopathological findings found.KB‐208 is similar to IVIG for the amelioration of ITP in multiple mouse strains. Chronic administration of KB‐208 for 60 days did not demonstrate in vivo toxicity. These findings indicate that KB‐208 is efficacious, without significant in vivo toxicities in mice, and is a potential small molecule candidate for further evaluation to be used in the treatment of ITP and possibly all immune cytopenias where phagocytosis is responsible for the pathophysiology. [ABSTRACT FROM AUTHOR]

The "New Journal of Chemistry" published by the Royal Society of Chemistry features a variety of research articles in the field of chemistry. The latest issue includes papers on topics such as the synthesis of acyclic analogues of adenosine sulfonamides, construction of covalent organic frameworks, and studies on various chemical compounds and reactions. The journal aims to connect the global chemistry community and invest profits back into the field of chemistry. [Extracted from the article]

Thiophene containing drugs have been developed and show up among many important drugs currently in the market, like Plavix®, Cymbalta® and Tomudex®. However, this important organic group is still not present in the fluorine fragment libraries found in the literature. To fix this and contribute to increasing the structural diversity of fragment libraries, we synthesized a fluorinated, bicyclic, thiophene-based fragment library in a modular fashion to be screened via ligand observed 19F NMR against drug targets. For certain compounds in the synthesized library, binding was detected against the HRAS mutant G12V, a notoriously undruggable cancer target, and further confirmed with 15N HSQC NMR experiments. To exclude the possibility that the binding was promiscuous, the best binding fragment was screened against an unrelated protein, RNase 5 and was shown to be a non-binder. [ABSTRACT FROM AUTHOR]

The efficient identification and validation of drug targets are paramount in drug discovery and development. Excessive costs, intricate procedures, and laborious sample handling frequently encumber contemporary methodologies. In this study, we introduce an innovative approach for the expeditious screening of drug targets utilizing solid-state nanopores. These nanopores provide a label-free, ultra-sensitive, and high-resolution platform for the real-time detection of biomolecular interactions. By observing the changes in relative ion currents over time after mixing different peptides with small molecule drugs, and supplementing this with noise analysis, we can pinpoint specific regions of drug action, thereby enhancing both the speed and cost-efficiency of drug development. This research offers novel insights into drug discovery, expands current perspectives, and lays the groundwork for formulating effective therapeutic strategies across a spectrum of diseases. [ABSTRACT FROM AUTHOR]

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFβ-stimulated control (1 μM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification. [ABSTRACT FROM AUTHOR]

Thiophene containing drugs have been developed and show up among many important drugs currently in the market, like Plavix®, Cymbalta® and Tomudex®. However, this important organic group is still not present in the fluorine fragment libraries found in the literature. To fix this and contribute to increasing the structural diversity of fragment libraries, we synthesized a fluorinated, bicyclic, thiophene-based fragment library in a modular fashion to be screened via ligand observed 19F NMR against drug targets. For certain compounds in the synthesized library, binding was detected against the HRAS mutant G12V, a notoriously undruggable cancer target, and further confirmed with 15N HSQC NMR experiments. To exclude the possibility that the binding was promiscuous, the best binding fragment was screened against an unrelated protein, RNase 5 and was shown to be a non-binder. [ABSTRACT FROM AUTHOR]

Over the past ~ 25 years, chemoinformatics has evolved as a scientific discipline, with a strong foundation in pharmaceutical research and scientific roots that can be traced back to the late 1950s. It covers a wide methodological spectrum and is perhaps best positioned in the greater context of chemical information science. Herein, the chemoinformatics discipline is delineated, characteristic (and partly problematic) features are discussed, and a global view of the field is provided, emphasizing key developments. [ABSTRACT FROM AUTHOR]

Conventional cell culture techniques generally fail to recapitulate the expression profiles or functional phenotypes of the in vivo equivalents they are meant to model. These cell culture models are indispensable for preclinical drug discovery and mechanistic studies. However, if our goal is to develop effective therapies that work as intended in the human body, we must revise our cell culture models to recapitulate normal and disease physiology to ensure that we identify compounds that are useful and effective beyond our in vitro models. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus is a metabolic disease categorized by hyperglycemia, resistance to insulin, and ß-cell dysfunction. Around the globe, approximately 422 million people have diabetes, out of which 1.5 million die annually. In spite of innovative advancements in the treatment of diabetes, no biological drug has been known to successfully cure and avert its progression. Thereupon, natural drugs derived from plants are emerging as a novel therapeutic strategy to combat diseases like diabetes. Objective: The current study aims to investigate the antidiabetic potential of natural compounds of Oryza sativa L. indica (black rice) in disease treatment. Methods: Antioxidant activity and alpha amylase assays were performed to evaluate the therapeutic potential of the extract of Oryza sativa L. indica. Gas chromatography–mass spectrometry (GC–MS) was used for identification of constituents from the ethanol extract. ADMET profiling (absorption, distribution, metabolism, excretion, and toxicity), network pharmacology, and molecular dynamics simulation were employed in order to uncover the active ingredients and their therapeutic targets in O. sativa L. indica against type 2 diabetes mellitus. Results: GC–MS of the plant extract provided a list of 184 compounds. Lipinski filter and toxicity parameters screened out 18 compounds. The topological parameters of the protein–protein interaction (PPI) were used to shortlist the nine key proteins (STAT3, HSP90AA1, AKT1, SRC, ESR1, MAPK1, NFKB1, EP300, and CREBBP) in the type 2 diabetes mellitus pathways. Later, molecular docking analysis and simulations showed that C14 (1H-purine-8-propanoic acid,.alpha.-amino-2, 3, 6, 7-tetrahydro-1,3,7-trimethyl-2,6-dioxo-) and C18 (cyclohexane-carboxamide, N-furfuryl) bind with AKT1 and ESR1 with a binding energy of 8.1, 6.9, 7.3, and 7.2 kcal/mol, respectively. RMSD (root-mean-square deviation) and RMSF (root-mean-square fluctuation) values for AKT1 and ESR1 have shown very little fluctuation, indicating that proteins were stabilized after ligand docking. Conclusion: This study suggests therapeutic drug candidates against AKT1 and ESR1 to treat type 2 diabetes mellitus. However, further wet-lab analysis is required to discover the best remedy for type 2 diabetes mellitus. [ABSTRACT FROM AUTHOR]

Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug–target interaction pairs. In this study, we propose a novel drug–target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug–target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations. [ABSTRACT FROM AUTHOR]

Triterpenes are structurally complex natural products with promising therapeutic properties. Recalcitrance to chemical synthesis has hindered their use in drug development. Recent advances now make it possible to access and harness triterpene structural diversity using engineering biology approaches, enabling the discovery and optimisation of a new generation of drug leads. [ABSTRACT FROM AUTHOR]

Reversible, non-covalent small molecule protein interactions are central to our understanding of biology and the development of therapeutics. Globally assessing all binding sites of a particular small molecule in a given system can be critical to defining mechanisms of action and assessing off-target effects. Emerging techniques to quantitatively map small molecule binding sites, largely driven by photoaffinity labeling (PAL) approaches, are expanding our knowledge of the ligandable proteome. Integrating small molecule binding site mapping with other technologies [e.g., structural modeling, artificial intelligence/machine learning (AI/ML)] will continue to improve our understanding of small molecule–protein interactions. The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule–protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this opinion article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development. The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule–protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this review article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development. [ABSTRACT FROM AUTHOR]

The development of new drugs is a vital effort that has the potential to improve human health, well-being and life expectancy. Molecular property prediction is a crucial step in drug discovery, as it helps to identify potential therapeutic compounds. However, experimental methods for drug development can often be time-consuming and resource-intensive, with a low probability of success. To address such limitations, deep learning (DL) methods have emerged as a viable alternative due to their ability to identify high-discriminating patterns in molecular data. In particular, graph neural networks (GNNs) operate on graph-structured data to identify promising drug candidates with desirable molecular properties. These methods represent molecules as a set of node (atoms) and edge (chemical bonds) features to aggregate local information for molecular graph representation learning. Despite the availability of several GNN frameworks, each approach has its own shortcomings. Although, some GNNs may excel in certain tasks, they may not perform as well in others. In this work, we propose a hybrid approach that incorporates different graph-based methods to combine their strengths and mitigate their limitations to accurately predict molecular properties. The proposed approach consists in a multi-layered hybrid GNN architecture that integrates multiple GNN frameworks to compute graph embeddings for molecular property prediction. Furthermore, we conduct extensive experiments on multiple benchmark datasets to demonstrate that our hybrid approach significantly outperforms the state-of-the-art graph-based models. The data and code scripts to reproduce the results are available in the repository, https://github.com/pedro-quesado/HybridGNN. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein–protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type–specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. Key messages: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD. [ABSTRACT FROM AUTHOR]

The reproducibility of chemical reactions, when obtaining protocols from literature or databases, is highly challenging for academicians, industry professionals and even now for the machine learning process. To synthesize the organic molecule under the photochemical condition, several years for the reaction optimization, highly skilled manpower, long reaction time etc. are needed, resulting in non-affordability and slow down the research and development. Herein, we have introduced the DigiChemTree backed with the artificial intelligence to auto-optimize the photochemical reaction parameter and synthesizing the on demand library of the molecules in fast manner. Newly, auto-generated digital code was further tested for the late stage functionalization of the various active pharmaceutical ingredient. Light-induced reactions of diazo compounds have become crucial in organic synthesis and drug discovery, however, optimization of reaction conditions is still very time-consuming. Here, the authors develop a DigiChemTree platform using artificial intelligence to auto-optimize the photochemical reaction parameters and rapidly synthesize an on-demand library of molecules. [ABSTRACT FROM AUTHOR]

Several approaches can be used to genetically modify the human malaria parasite Plasmodium falciparum to express reporter proteins, such as fluorescent proteins and luciferases, and their expression can be controlled using stage-specific or constitutive promoters. The expression of fluorescent proteins in malaria parasites enables molecular analyses, such as live imaging, flow cytometry, and cell sorting. The number of parasites at each stage can be quantified using the luciferase luminescent signal to evaluate the antimalarial effects of the compounds of interest and the inhibitory effects of antibodies. Further parasite reporter lines are needed to unveil the biological aspects of human malaria parasites and boost drug discovery against this deadly pathogen. The human malaria parasite Plasmodium falciparum causes the most severe form of malaria in endemic regions and is transmitted via mosquito bites. To better understand the biology of this deadly pathogen, a variety of P. falciparum reporter lines have been generated using transgenic approaches to express reporter proteins, such as fluorescent proteins and luciferases. This review discusses the advances in recently generated P. falciparum transgenic reporter lines, which will aid in the investigation of parasite physiology and the discovery of novel antimalarial drugs. Future prospects for the generation of new and superior human malaria parasite reporter lines are also discussed, and unresolved questions in malaria biology are highlighted to help boost support for the development and implementation of malaria treatments. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is the most common form of arthritis and a leading cause of pain and disability in adults. A central feature is progressive cartilage degradation and matrix fragment formation driven by the excessive production of matrix metalloproteinases (MMPs), such as MMP-13, by articular chondrocytes. Inflammatory factors, including interleukin 6 (IL-6), are secreted into the joint by synovial fibroblasts, and can contribute to pain and inflammation. No therapeutic exists that addresses the underlying loss of joint tissue in OA. To address this, we developed and utilized a cell-based high-throughput OA drug discovery platform using normal human chondrocytes treated with a recombinant fragment of the matrix protein fibronectin (FN-f) as a catabolic stimulus relevant to OA pathogenesis and a readout using a fluorescent MMP-13 responsive probe. The goal was to test this screening platform by identifying compounds that inhibited FN-f-induced MMP-13 production and determine if these compounds also inhibited catabolic signaling in OA chondrocytes and synovial fibroblasts. Two pilot screens of 1344 small molecules revealed five "hits" that strongly inhibited FN-f induced MMP-13 production with low cytotoxicity. These included RO-3306 (CDK1 inhibitor (i)), staurosporine (PKCi), trametinib (MEK1 and MEK2i), GSK-626616 (DYRK3i), and edicotinib (CSF-1Ri). Secondary testing using immunoblots and cells derived from OA joint tissues confirmed the ability of selected compounds to inhibit chondrocyte MMP-13 production and FN-f stimulated IL-6 production by synovial fibroblasts. These findings support the use of this high throughput screening assay for discovery of disease-modifying osteoarthritis drugs. [ABSTRACT FROM AUTHOR]